The Experts below are selected from a list of 15336 Experts worldwide ranked by ideXlab platform

Gina R Kuperberg - One of the best experts on this subject based on the ideXlab platform.

  • neural evidence for faster and further automatic Spreading Activation in schizophrenic thought disorder
    Schizophrenia Bulletin, 2007
    Co-Authors: Donna A Kreher, Phillip J Holcomb, Donald C Goff, Gina R Kuperberg
    Abstract:

    It has been proposed that the loose associations characteristic of thought disorder in schizophrenia result from an abnormal increase in the automatic spread of Activation through semantic memory. We tested this hypothesis by examining the time course of neural semantic priming using event-related potentials (ERPs). ERPs were recorded to target words that were directly related, indirectly related, and unrelated to their preceding primes, while thought-disordered (TD) and non-TD schizophrenia patients and healthy controls performed an implicit semantic categorization task under experimental conditions that encouraged automatic processing. By 300-400 milliseconds after target word onset, TD patients showed increased indirect semantic priming relative to non-TD patients and healthy controls, while the degree of direct semantic priming was increased in only the most severely TD patients. By 400-500 milliseconds after target word onset, both direct and indirect semantic priming were generally equivalent across the 3 groups. These findings demonstrate for the first time at a neural level that, under automatic conditions, Activation across the semantic network spreads further within a shorter period of time in specific association with positive thought disorder in schizophrenia.

Gregory J. Trafton - One of the best experts on this subject based on the ideXlab platform.

  • a long term memory competitive process model of a common procedural error
    Cognitive Science, 2013
    Co-Authors: Franklin P Tamborello, Gregory J. Trafton
    Abstract:

    Abstract : A novel computational cognitive model explains human procedural error in terms of declarative memory processes. This is an early version of a process model intended to predict and explain multiple classes of procedural error a priori. We begin with postcompletion error (PCE) a type of systematic procedural error that people are prone to commit when there is one step to perform after they have accomplished their main task goal. Participants in an experiment demonstrated increased PCE rates following an interruption in a realistic form-filling task. The model explains PCE as a consequence of two declarative retrieval processes, Spreading Activation and base-level Activation, competing with each other because of features of task and working memory structure. Our intention is to generalize the model to other classes of procedural error in complex task environments.

Franklin P Tamborello - One of the best experts on this subject based on the ideXlab platform.

  • a long term memory competitive process model of a common procedural error part ii working memory load and capacity
    2013
    Co-Authors: Franklin P Tamborello, J. Greg Trafton
    Abstract:

    Abstract : Postcompletion error (PCE) is a type of systematic procedural error that people are prone to commit when there is one step to perform after they have accomplished their main task goal. A computational cognitive model developed previously for PCE in an interruption paradigm extends to a working memory load and capacity paradigm. The model explains PCE in terms of long-term declarative memory mechanisms opposing base-level Activation with Spreading Activation.

  • a long term memory competitive process model of a common procedural error
    Cognitive Science, 2013
    Co-Authors: Franklin P Tamborello, Gregory J. Trafton
    Abstract:

    Abstract : A novel computational cognitive model explains human procedural error in terms of declarative memory processes. This is an early version of a process model intended to predict and explain multiple classes of procedural error a priori. We begin with postcompletion error (PCE) a type of systematic procedural error that people are prone to commit when there is one step to perform after they have accomplished their main task goal. Participants in an experiment demonstrated increased PCE rates following an interruption in a realistic form-filling task. The model explains PCE as a consequence of two declarative retrieval processes, Spreading Activation and base-level Activation, competing with each other because of features of task and working memory structure. Our intention is to generalize the model to other classes of procedural error in complex task environments.

Stuckenschmidt Heiner - One of the best experts on this subject based on the ideXlab platform.

  • A Spreading Activation framework for tracking conceptual complexity of texts
    Association for Computational Linguistics ACL, 2019
    Co-Authors: Hulpus Ioana, Štajner Sanja, Stuckenschmidt Heiner
    Abstract:

    We propose an unsupervised approach for assessing conceptual complexity of texts, based on Spreading Activation. Using DBpedia knowledge graph as a proxy to long-term memory, mentioned concepts become activated and trigger further Activation as the text is sequentially traversed. Drawing inspiration from psycholinguistic theories of reading comprehension, we model memory processes such as semantic priming, sentence wrap-up, and forgetting. We show that our models capture various aspects of conceptual text complexity and significantly outperform current state of the art

Ion Androutsopoulos - One of the best experts on this subject based on the ideXlab platform.

  • word sense disambiguation with Spreading Activation networks generated from thesauri
    International Joint Conference on Artificial Intelligence, 2007
    Co-Authors: George Tsatsaronis, Michalis Vazirgiannis, Ion Androutsopoulos
    Abstract:

    Most word sense disambiguation (WSD) methods require large quantities of manually annotated training data and/or do not exploit fully the semantic relations of thesauri. We propose a new unsupervised WSD algorithm, which is based on generating Spreading Activation Networks (SANs) from the senses of a thesaurus and the relations between them. A new method of assigning weights to the networks' links is also proposed. Experiments show that the algorithm outperforms previous unsupervised approaches to WSD.